Existing Solutions. In recent years, many anomaly detection methods have been proposed that utilize deep autoencoders (more details in Section ). However, the models that have the best detection scores often have a very high memory footprint (4-6 GB for training and inference in state-of-the-art approaches). Some prior work aims at reducing the on-device parameter size of such models using techniques like model compression [1]}, neural network slimming [2]} or model distribution [3]}. However, such approaches usually have performance penalties associated with them.
